library(survival)
library(FRESA.CAD)
## Loading required package: Rcpp
## Loading required package: stringr
## Loading required package: miscTools
## Loading required package: Hmisc
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
## Loading required package: pROC
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('keep.trailing.zeros',TRUE)
data(lung)
## Warning in data(lung): data set 'lung' not found
lung$inst <- NULL
lung$status <- lung$status - 1
lung <- lung[complete.cases(lung),]
pander::pander(table(lung$status))
| 0 | 1 |
|---|---|
| 47 | 121 |
pander::pander(summary(lung$time))
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 5 | 175 | 268 | 310 | 416 | 1022 |
convar <- colnames(lung)[lapply(apply(lung,2,unique),length) > 10]
convar <- convar[convar != "time"]
topvar <- univariate_BinEnsemble(lung[,c("status",convar)],"status")
pander::pander(topvar)
| age | wt.loss |
|---|---|
| 0.106 | 0.106 |
topv <- min(5,length(topvar))
topFive <- names(topvar)[1:topv]
RRanalysis <- list();
idx <- 1
for (topf in topFive)
{
RRanalysis[[idx]] <- RRPlot(cbind(lung$status,lung[,topf]),
atProb=c(0.90),
timetoEvent=lung$time,
title=topf,
# plotRR=FALSE
)
idx <- idx + 1
}
names(RRanalysis) <- topFive
ROCAUC <- NULL
CstatCI <- NULL
RRatios <- NULL
LogRangp <- NULL
Sensitivity <- NULL
Specificity <- NULL
for (topf in topFive)
{
CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
RRatios <- rbind(RRatios,RRanalysis[[topf]]$RR_atP)
LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
}
rownames(CstatCI) <- topFive
rownames(RRatios) <- topFive
rownames(LogRangp) <- topFive
rownames(Sensitivity) <- topFive
rownames(Specificity) <- topFive
rownames(ROCAUC) <- topFive
pander::pander(ROCAUC)
| est | lower | upper | |
|---|---|---|---|
| age | 0.586 | 0.489 | 0.683 |
| wt.loss | 0.558 | 0.459 | 0.656 |
pander::pander(CstatCI)
| mean.C Index | median | lower | upper | |
|---|---|---|---|---|
| age | 0.558 | 0.558 | 0.504 | 0.614 |
| wt.loss | 0.511 | 0.510 | 0.451 | 0.571 |
pander::pander(RRatios)
| est | lower | upper | |
|---|---|---|---|
| age | 0.997 | 0.742 | 1.34 |
| wt.loss | 0.785 | 0.462 | 1.33 |
pander::pander(LogRangp)
| age | 0.704 |
| wt.loss | 0.358 |
pander::pander(Sensitivity)
| est | lower | upper | |
|---|---|---|---|
| age | 0.1157 | 0.0647 | 0.187 |
| wt.loss | 0.0496 | 0.0184 | 0.105 |
pander::pander(Specificity)
| est | lower | upper | |
|---|---|---|---|
| age | 0.894 | 0.769 | 0.965 |
| wt.loss | 0.894 | 0.769 | 0.965 |
meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1],RRatios[,1])
colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe","RR")
pander::pander(meanMatrix)
| ROCAUC | C-Stat | Sen | Spe | RR | |
|---|---|---|---|---|---|
| age | 0.586 | 0.558 | 0.1157 | 0.894 | 0.997 |
| wt.loss | 0.558 | 0.511 | 0.0496 | 0.894 | 0.785 |
ml <- BSWiMS.model(Surv(time,status)~1,data=lung,NumberofRepeats = 10)
[+++++++++++++++++++++++++++]..
sm <- summary(ml)
pander::pander(sm$coefficients)
| Estimate | lower | HR | upper | u.Accuracy | r.Accuracy | |
|---|---|---|---|---|---|---|
| ph.ecog | 4.32e-01 | 1.194 | 1.541 | 1.988 | 0.679 | 0.649 |
| sex | -4.59e-01 | 0.456 | 0.632 | 0.876 | 0.649 | 0.679 |
| pat.karno | -1.77e-03 | 0.997 | 0.998 | 1.000 | 0.506 | 0.720 |
| ph.karno | -4.06e-07 | 1.000 | 1.000 | 1.000 | 0.577 | 0.720 |
| full.Accuracy | u.AUC | r.AUC | full.AUC | IDI | NRI | |
|---|---|---|---|---|---|---|
| ph.ecog | 0.601 | 0.601 | 0.620 | 0.600 | 0.0449 | 0.405 |
| sex | 0.601 | 0.620 | 0.601 | 0.600 | 0.0285 | 0.478 |
| pat.karno | 0.506 | 0.585 | 0.500 | 0.585 | 0.0292 | 0.342 |
| ph.karno | 0.577 | 0.570 | 0.500 | 0.570 | 0.0143 | 0.280 |
| z.IDI | z.NRI | Delta.AUC | Frequency | |
|---|---|---|---|---|
| ph.ecog | 3.33 | 2.48 | -0.02005 | 1.0 |
| sex | 2.76 | 2.85 | -0.00167 | 1.0 |
| pat.karno | 2.44 | 2.24 | 0.08546 | 1.0 |
| ph.karno | 2.22 | 1.64 | 0.06998 | 0.7 |
Here we evaluate the model using the RRPlot() function.
Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years
timeinterval <- 2*mean(subset(lung,status==1)$time)
h0 <- sum(lung$status & lung$time <= timeinterval)
h0 <- h0/sum((lung$time > timeinterval) | (lung$status==1))
pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")
| h0 | timeinterval |
|---|---|
| 0.85 | 578 |
index <- predict(ml,lung)
rdata <- cbind(lung$status,ppoisGzero(index,h0))
rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90),
timetoEvent=lung$time,
title="Raw Train: Lung Cancer",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
As we can see the Observed probability as well as the Time vs. Events are not calibrated.
pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.649 | 0.475 | 0.339 | 0.339 | 0.493 |
| RR | 1.280 | 1.789 | 60.500 | 60.500 | 1.312 |
| SEN | 0.306 | 0.843 | 1.000 | 1.000 | 0.636 |
| SPE | 0.872 | 0.489 | 0.170 | 0.170 | 0.596 |
| BACC | 0.589 | 0.666 | 0.585 | 0.585 | 0.616 |
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.65 | 1.37 | 1.97 | 3.16e-07 |
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.23 | 1.23 | 1.19 | 1.27 |
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.21 | 1.21 | 1.2 | 1.22 |
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.652 | 0.65 | 0.585 | 0.709 |
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.69 | 0.597 | 0.783 |
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.306 | 0.225 | 0.396 |
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.872 | 0.743 | 0.952 |
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
| 90% | at_max_BACC | at_max_RR | atSPE100 | at_0.5 |
|---|---|---|---|---|
| 0.649 | 0.475 | 0.339 | 0.339 | 0.5 |
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.28 | 1.08 | 1.52 |
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 125 | 84 | 96.5 | 1.61 | 8.01 |
| class=1 | 43 | 37 | 24.5 | 6.34 | 8.01 |
op <- par(no.readonly = TRUE)
calprob <- CoxRiskCalibration(ml,lung,"status","time")
pander::pander(c(h0=calprob$h0,
Gain=calprob$hazardGain,
DeltaTime=calprob$timeInterval),
caption="Cox Calibration Parameters")
| h0 | Gain | DeltaTime |
|---|---|---|
| 1.29 | 1.52 | 749 |
h0 <- calprob$h0
timeinterval <- calprob$timeInterval;
rdata <- cbind(lung$status,calprob$prob)
rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90),
timetoEvent=lung$time,
title="Train: Lung",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.796 | 0.624 | 0.467 | 0.467 | 0.479 |
| RR | 1.280 | 1.789 | 60.500 | 60.500 | 2.784 |
| SEN | 0.306 | 0.843 | 1.000 | 1.000 | 0.959 |
| SPE | 0.872 | 0.489 | 0.170 | 0.170 | 0.277 |
| BACC | 0.589 | 0.666 | 0.585 | 0.585 | 0.618 |
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.45 | 1.2 | 1.73 | 0.000124 |
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.06 | 1.06 | 1.02 | 1.09 |
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.01 | 1.01 | 1 | 1.01 |
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.652 | 0.651 | 0.587 | 0.711 |
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.691 | 0.598 | 0.784 |
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.306 | 0.225 | 0.396 |
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.872 | 0.743 | 0.952 |
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
| 90% | at_max_BACC | at_max_RR | atSPE100 | at_0.5 |
|---|---|---|---|---|
| 0.796 | 0.624 | 0.467 | 0.467 | 0.5 |
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.28 | 1.08 | 1.52 |
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 125 | 84 | 96.5 | 1.61 | 8.01 |
| class=1 | 43 | 37 | 24.5 | 6.34 | 8.01 |
rcv <- randomCV(theData=lung,
theOutcome = Surv(time,status)~1,
fittingFunction=BSWiMS.model,
trainFraction = 0.95,
repetitions=200,
classSamplingType = "Pro"
)
.[+++].[++].[+++].[+++].[+++].[++].[+++].[+++].[+++].[+++]10 Tested: 72 Avg. Selected: 3.8 Min Tests: 1 Max Tests: 4 Mean Tests: 1.388889 . MAD: 0.4728702
.[++].[++++].[++].[+++].[++].[+++].[+].[+++].[+++].[++++]20 Tested: 118 Avg. Selected: 3.75 Min Tests: 1 Max Tests: 6 Mean Tests: 1.694915 . MAD: 0.474057
.[+++].[+++].[++].[++].[+++].[+++].[+++].[+++].[+++].[+++]30 Tested: 141 Avg. Selected: 3.8 Min Tests: 1 Max Tests: 8 Mean Tests: 2.12766 . MAD: 0.4730948
.[++-].[++].[+++].[+++].[++].[++-].[+++].[++].[++].[+++]40 Tested: 152 Avg. Selected: 3.7 Min Tests: 1 Max Tests: 8 Mean Tests: 2.631579 . MAD: 0.4741662
.[+++].[+++].[++-].[+].[+++].[+++].[+++].[+++].[++].[+++]50 Tested: 157 Avg. Selected: 3.68 Min Tests: 1 Max Tests: 9 Mean Tests: 3.184713 . MAD: 0.4754415
.[++].[+++].[+++].[+++].[+++].[+++].[+++].[++].[++].[+++]60 Tested: 160 Avg. Selected: 3.683333 Min Tests: 1 Max Tests: 9 Mean Tests: 3.75 . MAD: 0.4776102
.[+++].[++].[+++].[++].[++++].[+++].[+++].[+++].[+++].[++]70 Tested: 165 Avg. Selected: 3.7 Min Tests: 1 Max Tests: 11 Mean Tests: 4.242424 . MAD: 0.4780849
.[++].[+++].[+++].[+++].[+++].[++].[++].[+++].[+++].[+++]80 Tested: 167 Avg. Selected: 3.7 Min Tests: 1 Max Tests: 12 Mean Tests: 4.790419 . MAD: 0.477782
.[+++].[+++].[++].[+++].[+++].[++].[+++].[+++].[+++].[++]90 Tested: 167 Avg. Selected: 3.7 Min Tests: 1 Max Tests: 14 Mean Tests: 5.389222 . MAD: 0.4774375
.[+++].[++].[+++-].[++].[++].[++-].[+++].[++].[++].[+++]100 Tested: 168 Avg. Selected: 3.67 Min Tests: 1 Max Tests: 18 Mean Tests: 5.952381 . MAD: 0.4761966
.[+++].[+++].[++].[+++].[+++].[++].[+++].[+-].[+].[+]110 Tested: 168 Avg. Selected: 3.627273 Min Tests: 1 Max Tests: 19 Mean Tests: 6.547619 . MAD: 0.4758525
.[++].[+++].[+++].[+++].[++].[+++].[+++].[+++].[+++].[++++]120 Tested: 168 Avg. Selected: 3.65 Min Tests: 2 Max Tests: 20 Mean Tests: 7.142857 . MAD: 0.4756308
.[+++].[+++].[+++].[+++].[+++].[+++].[++].[+++].[+++].[+++]130 Tested: 168 Avg. Selected: 3.669231 Min Tests: 2 Max Tests: 20 Mean Tests: 7.738095 . MAD: 0.4757132
.[+++].[+++].[+++].[+++].[+++].[++++].[+++].[++++].[+++].[+++]140 Tested: 168 Avg. Selected: 3.707143 Min Tests: 2 Max Tests: 20 Mean Tests: 8.333333 . MAD: 0.4756489
.[+++].[+++].[+++].[+++].[++].[+++].[++].[++].[++].[++]150 Tested: 168 Avg. Selected: 3.693333 Min Tests: 2 Max Tests: 20 Mean Tests: 8.928571 . MAD: 0.4755712
.[++].[+++].[++].[+++].[++].[+++].[++].[++].[++].[+++]160 Tested: 168 Avg. Selected: 3.675 Min Tests: 3 Max Tests: 21 Mean Tests: 9.52381 . MAD: 0.4752559
.[+++].[++].[+++].[++].[+++].[++].[+++].[++].[+++].[+++]170 Tested: 168 Avg. Selected: 3.670588 Min Tests: 3 Max Tests: 21 Mean Tests: 10.11905 . MAD: 0.4753232
.[+++].[++].[+++].[+++].[++].[+++].[+].[+++].[+++].[+++]180 Tested: 168 Avg. Selected: 3.666667 Min Tests: 3 Max Tests: 22 Mean Tests: 10.71429 . MAD: 0.47569
.[+++].[+++].[++].[++++].[+++].[++].[+++].[++].[+++].[+++]190 Tested: 168 Avg. Selected: 3.673684 Min Tests: 4 Max Tests: 23 Mean Tests: 11.30952 . MAD: 0.4757949
.[+++].[++].[+++].[+++].[+++].[+++].[++++].[+++].[++].[+++]200 Tested: 168 Avg. Selected: 3.685 Min Tests: 4 Max Tests: 25 Mean Tests: 11.90476 . MAD: 0.4761283
stp <- rcv$survTestPredictions
stp <- stp[!is.na(stp[,4]),]
bbx <- boxplot(unlist(stp[,1])~rownames(stp),plot=FALSE)
times <- bbx$stats[3,]
status <- boxplot(unlist(stp[,2])~rownames(stp),plot=FALSE)$stats[3,]
prob <- ppoisGzero(boxplot(unlist(stp[,4])~rownames(stp),plot=FALSE)$stats[3,],h0)
rdatacv <- cbind(status,prob)
rownames(rdatacv) <- bbx$names
names(times) <- bbx$names
rrAnalysisTest <- RRPlot(rdatacv,atProb=c(0.90),
timetoEvent=times,
title="Test: Lung Cancer",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.803 | 0.610 | 0.610 | 0.4480 | 0.501 |
| RR | 1.232 | 2.958 | 2.958 | 7.2455 | 2.612 |
| SEN | 0.231 | 0.959 | 0.959 | 1.0000 | 0.959 |
| SPE | 0.894 | 0.298 | 0.298 | 0.0213 | 0.255 |
| BACC | 0.563 | 0.628 | 0.628 | 0.5106 | 0.607 |
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.45 | 1.2 | 1.73 | 0.000124 |
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.05 | 1.05 | 1.01 | 1.09 |
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 0.952 | 0.952 | 0.943 | 0.961 |
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.602 | 0.601 | 0.538 | 0.666 |
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.609 | 0.51 | 0.709 |
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.231 | 0.16 | 0.317 |
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.894 | 0.769 | 0.965 |
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
| 90% | at_max_BACC | at_max_RR | atSPE100 | at_0.5 |
|---|---|---|---|---|
| 0.802 | 0.61 | 0.61 | 0.448 | 0.5 |
pander::pander(t(rrAnalysisTest$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.23 | 1.03 | 1.48 |
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 135 | 93 | 102.1 | 0.817 | 5.31 |
| class=1 | 33 | 28 | 18.9 | 4.421 | 5.31 |
rdatacv <- cbind(status,prob,times)
calprob <- CalibrationProbPoissonRisk(rdatacv)
pander::pander(c(h0=calprob$h0,
Gain=calprob$hazardGain,
DeltaTime=calprob$timeInterval),
caption="Cox Calibration Parameters")
| h0 | Gain | DeltaTime |
|---|---|---|
| 0.85 | 1 | 754 |
timeinterval <- calprob$timeInterval;
rdata <- cbind(status,calprob$prob)
rrAnalysisTest <- RRPlot(rdata,atProb=c(0.90),
timetoEvent=times,
title="Calibrated Test: Lung",
ysurvlim=c(0.00,1.0),
riskTimeInterval=timeinterval)
pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
| @:0.9 | @MAX_BACC | @MAX_RR | @SPE100 | p(0.5) | |
|---|---|---|---|---|---|
| Thr | 0.803 | 0.610 | 0.610 | 0.4480 | 0.501 |
| RR | 1.232 | 2.958 | 2.958 | 7.2455 | 2.612 |
| SEN | 0.231 | 0.959 | 0.959 | 1.0000 | 0.959 |
| SPE | 0.894 | 0.298 | 0.298 | 0.0213 | 0.255 |
| BACC | 0.563 | 0.628 | 0.628 | 0.5106 | 0.607 |
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
| O/E | Low | Upper | p.value |
|---|---|---|---|
| 1.45 | 1.21 | 1.74 | 9.57e-05 |
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 1.06 | 1.06 | 1.02 | 1.09 |
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
| mean | 50% | 2.5% | 97.5% |
|---|---|---|---|
| 0.952 | 0.952 | 0.943 | 0.961 |
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.602 | 0.6 | 0.535 | 0.664 |
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
| est | lower | upper |
|---|---|---|
| 0.609 | 0.51 | 0.709 |
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
| est | lower | upper |
|---|---|---|
| 0.231 | 0.16 | 0.317 |
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
| est | lower | upper |
|---|---|---|
| 0.894 | 0.769 | 0.965 |
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
| 90% | at_max_BACC | at_max_RR | atSPE100 | at_0.5 |
|---|---|---|---|---|
| 0.802 | 0.61 | 0.61 | 0.448 | 0.5 |
pander::pander(t(rrAnalysisTest$RR_atP),caption="Risk Ratio")
| est | lower | upper |
|---|---|---|
| 1.23 | 1.03 | 1.48 |
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 135 | 93 | 102.1 | 0.817 | 5.31 |
| class=1 | 33 | 28 | 18.9 | 4.421 | 5.31 |